{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# HAR CNN training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import os\n",
    "from utils.utilities import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, labels_train, list_ch_train = read_data(data_path=\"./data/\", split=\"train\") # train\n",
    "X_test, labels_test, list_ch_test = read_data(data_path=\"./data/\", split=\"test\") # test\n",
    "\n",
    "assert list_ch_train == list_ch_test, \"Mistmatch in channels!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Normalize\n",
    "X_train, X_test = standardize(X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Train/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_tr, X_vld, lab_tr, lab_vld = train_test_split(X_train, labels_train, \n",
    "                                                stratify = labels_train, random_state = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One-hot encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_tr = one_hot(lab_tr)\n",
    "y_vld = one_hot(lab_vld)\n",
    "y_test = one_hot(labels_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "batch_size = 600       # Batch size\n",
    "seq_len = 128          # Number of steps\n",
    "learning_rate = 0.0001\n",
    "epochs =1000\n",
    "\n",
    "n_classes = 6\n",
    "n_channels = 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Construct the graph\n",
    "Placeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "graph = tf.Graph()\n",
    "\n",
    "# Construct placeholders\n",
    "with graph.as_default():\n",
    "    inputs_ = tf.placeholder(tf.float32, [None, seq_len, n_channels], name = 'inputs')\n",
    "    labels_ = tf.placeholder(tf.float32, [None, n_classes], name = 'labels')\n",
    "    keep_prob_ = tf.placeholder(tf.float32, name = 'keep')\n",
    "    learning_rate_ = tf.placeholder(tf.float32, name = 'learning_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Build Convolutional Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    # (batch, 128, 9) --> (batch, 64, 18)\n",
    "    conv1 = tf.layers.conv1d(inputs=inputs_, filters=18, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2, padding='same')\n",
    "    \n",
    "    # (batch, 64, 18) --> (batch, 32, 18)\n",
    "    conv2 = tf.layers.conv1d(inputs=max_pool_1, filters=18, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2, padding='same')\n",
    "    \n",
    "    # (batch, 32, 18) --> (batch, 16, 36)\n",
    "    conv3 = tf.layers.conv1d(inputs=max_pool_2, filters=36, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_3 = tf.layers.max_pooling1d(inputs=conv3, pool_size=2, strides=2, padding='same')\n",
    "    \n",
    "    # (batch, 16, 36) --> (batch, 8, 36)\n",
    "    conv4 = tf.layers.conv1d(inputs=max_pool_3, filters=36, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_4 = tf.layers.max_pooling1d(inputs=conv4, pool_size=2, strides=2, padding='same')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Build the inception layer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/HAR_inception.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    \n",
    "    # convolution: input to output of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 36)\n",
    "    conv1_11 = tf.layers.conv1d(inputs=max_pool_4, filters=36, kernel_size=1, strides=1,\n",
    "                               padding='same', activation = tf.nn.relu)\n",
    "    \n",
    "    # convolution: input to middle layer of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 18)\n",
    "    conv1_21 = tf.layers.conv1d(inputs=max_pool_4, filters=18, kernel_size=1, strides=1,\n",
    "                               padding='same', activation = tf.nn.relu)\n",
    "    \n",
    "    # convolution: input to middle layer of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 18)\n",
    "    conv1_31 = tf.layers.conv1d(inputs=max_pool_4, filters=18, kernel_size=1, strides=1,\n",
    "                               padding='same', activation = tf.nn.relu)\n",
    "    \n",
    "    # average pool: input to middle layer of inception\n",
    "    # (batch, 8, 36) --> (batch, 8, 36)\n",
    "    avg_pool_41 = tf.layers.average_pooling1d(inputs=max_pool_4, pool_size=2, strides=1, padding='same')\n",
    "    \n",
    "    ## Middle layer of inception\n",
    "    \n",
    "    # convolution: middle to out layer of inception (size=2)\n",
    "    # (batch, 8, 18) --> (batch, 8, 36)\n",
    "    conv2_22 = tf.layers.conv1d(inputs=conv1_21, filters=36, kernel_size=2, strides=1,\n",
    "                               padding='same', activation=tf.nn.relu)\n",
    "    \n",
    "    # convolution: middle to out layer of inception (size=4)\n",
    "    # (batch, 8, 18) --> (batch, 8, 36)\n",
    "    conv4_32 = tf.layers.conv1d(inputs=conv1_31, filters=36, kernel_size=4, strides=1,\n",
    "                               padding='same', activation=tf.nn.relu)\n",
    "    \n",
    "    # convolution: middle to out layer of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 36)\n",
    "    conv1_42 = tf.layers.conv1d(inputs=avg_pool_41, filters=36, kernel_size=1, strides=1,\n",
    "                               padding='same', activation=tf.nn.relu)\n",
    "    \n",
    "    ## Out layer: Concatenate filters\n",
    "    # (batch, 8, 4*36)\n",
    "    inception_out = tf.concat([conv1_11, conv2_22, conv4_32, conv1_42], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, flatten and pass to the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    # Flatten and add dropout\n",
    "    flat = tf.reshape(inception_out, (-1, 8*144))\n",
    "    flat = tf.nn.dropout(flat, keep_prob=keep_prob_)\n",
    "    \n",
    "    # Predictions\n",
    "    logits = tf.layers.dense(flat, n_classes)\n",
    "    \n",
    "    # Cost function and optimizer\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels_))\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate_).minimize(cost)\n",
    "    \n",
    "    # Accuracy\n",
    "    correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(labels_, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Train the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "if (os.path.exists('checkpoints-cnn') == False):\n",
    "    !mkdir checkpoints-cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/1000 Iteration: 5 Train loss: 1.807237 Train acc: 0.190000\n",
      "Epoch: 1/1000 Iteration: 10 Train loss: 1.781817 Train acc: 0.210000\n",
      "Epoch: 1/1000 Iteration: 10 Validation loss: 1.732405 Validation acc: 0.165556\n",
      "Epoch: 1/1000 Iteration: 15 Train loss: 1.735382 Train acc: 0.186667\n",
      "Epoch: 2/1000 Iteration: 20 Train loss: 1.707044 Train acc: 0.205000\n",
      "Epoch: 2/1000 Iteration: 20 Validation loss: 1.667085 Validation acc: 0.252778\n",
      "Epoch: 2/1000 Iteration: 25 Train loss: 1.692855 Train acc: 0.211667\n",
      "Epoch: 3/1000 Iteration: 30 Train loss: 1.663363 Train acc: 0.188333\n",
      "Epoch: 3/1000 Iteration: 30 Validation loss: 1.619117 Validation acc: 0.302778\n",
      "Epoch: 3/1000 Iteration: 35 Train loss: 1.636881 Train acc: 0.256667\n",
      "Epoch: 4/1000 Iteration: 40 Train loss: 1.613248 Train acc: 0.230000\n",
      "Epoch: 4/1000 Iteration: 40 Validation loss: 1.581009 Validation acc: 0.328889\n",
      "Epoch: 4/1000 Iteration: 45 Train loss: 1.597929 Train acc: 0.278333\n",
      "Epoch: 5/1000 Iteration: 50 Train loss: 1.565466 Train acc: 0.326667\n",
      "Epoch: 5/1000 Iteration: 50 Validation loss: 1.546932 Validation acc: 0.352778\n",
      "Epoch: 6/1000 Iteration: 55 Train loss: 1.565843 Train acc: 0.311667\n",
      "Epoch: 6/1000 Iteration: 60 Train loss: 1.560536 Train acc: 0.333333\n",
      "Epoch: 6/1000 Iteration: 60 Validation loss: 1.513695 Validation acc: 0.355556\n",
      "Epoch: 7/1000 Iteration: 65 Train loss: 1.515268 Train acc: 0.358333\n",
      "Epoch: 7/1000 Iteration: 70 Train loss: 1.511540 Train acc: 0.328333\n",
      "Epoch: 7/1000 Iteration: 70 Validation loss: 1.479393 Validation acc: 0.370556\n",
      "Epoch: 8/1000 Iteration: 75 Train loss: 1.504751 Train acc: 0.365000\n",
      "Epoch: 8/1000 Iteration: 80 Train loss: 1.502534 Train acc: 0.330000\n",
      "Epoch: 8/1000 Iteration: 80 Validation loss: 1.443342 Validation acc: 0.381111\n",
      "Epoch: 9/1000 Iteration: 85 Train loss: 1.469657 Train acc: 0.355000\n",
      "Epoch: 9/1000 Iteration: 90 Train loss: 1.471193 Train acc: 0.338333\n",
      "Epoch: 9/1000 Iteration: 90 Validation loss: 1.405088 Validation acc: 0.385556\n",
      "Epoch: 10/1000 Iteration: 95 Train loss: 1.416592 Train acc: 0.376667\n",
      "Epoch: 11/1000 Iteration: 100 Train loss: 1.394111 Train acc: 0.401667\n",
      "Epoch: 11/1000 Iteration: 100 Validation loss: 1.364348 Validation acc: 0.387222\n",
      "Epoch: 11/1000 Iteration: 105 Train loss: 1.385560 Train acc: 0.376667\n",
      "Epoch: 12/1000 Iteration: 110 Train loss: 1.337472 Train acc: 0.400000\n",
      "Epoch: 12/1000 Iteration: 110 Validation loss: 1.322166 Validation acc: 0.396667\n",
      "Epoch: 12/1000 Iteration: 115 Train loss: 1.322026 Train acc: 0.378333\n",
      "Epoch: 13/1000 Iteration: 120 Train loss: 1.340360 Train acc: 0.363333\n",
      "Epoch: 13/1000 Iteration: 120 Validation loss: 1.281044 Validation acc: 0.403889\n",
      "Epoch: 13/1000 Iteration: 125 Train loss: 1.319373 Train acc: 0.385000\n",
      "Epoch: 14/1000 Iteration: 130 Train loss: 1.318053 Train acc: 0.373333\n",
      "Epoch: 14/1000 Iteration: 130 Validation loss: 1.243519 Validation acc: 0.425000\n",
      "Epoch: 14/1000 Iteration: 135 Train loss: 1.294361 Train acc: 0.413333\n",
      "Epoch: 15/1000 Iteration: 140 Train loss: 1.217544 Train acc: 0.485000\n",
      "Epoch: 15/1000 Iteration: 140 Validation loss: 1.209808 Validation acc: 0.511667\n",
      "Epoch: 16/1000 Iteration: 145 Train loss: 1.228186 Train acc: 0.480000\n",
      "Epoch: 16/1000 Iteration: 150 Train loss: 1.194720 Train acc: 0.503333\n",
      "Epoch: 16/1000 Iteration: 150 Validation loss: 1.179951 Validation acc: 0.582778\n",
      "Epoch: 17/1000 Iteration: 155 Train loss: 1.183798 Train acc: 0.535000\n",
      "Epoch: 17/1000 Iteration: 160 Train loss: 1.185185 Train acc: 0.515000\n",
      "Epoch: 17/1000 Iteration: 160 Validation loss: 1.153052 Validation acc: 0.613333\n",
      "Epoch: 18/1000 Iteration: 165 Train loss: 1.205010 Train acc: 0.516667\n",
      "Epoch: 18/1000 Iteration: 170 Train loss: 1.169736 Train acc: 0.558333\n",
      "Epoch: 18/1000 Iteration: 170 Validation loss: 1.128057 Validation acc: 0.639444\n",
      "Epoch: 19/1000 Iteration: 175 Train loss: 1.164483 Train acc: 0.570000\n",
      "Epoch: 19/1000 Iteration: 180 Train loss: 1.185942 Train acc: 0.560000\n",
      "Epoch: 19/1000 Iteration: 180 Validation loss: 1.102286 Validation acc: 0.666667\n",
      "Epoch: 20/1000 Iteration: 185 Train loss: 1.101028 Train acc: 0.605000\n",
      "Epoch: 21/1000 Iteration: 190 Train loss: 1.110404 Train acc: 0.606667\n",
      "Epoch: 21/1000 Iteration: 190 Validation loss: 1.075242 Validation acc: 0.677778\n",
      "Epoch: 21/1000 Iteration: 195 Train loss: 1.094760 Train acc: 0.593333\n",
      "Epoch: 22/1000 Iteration: 200 Train loss: 1.054406 Train acc: 0.600000\n",
      "Epoch: 22/1000 Iteration: 200 Validation loss: 1.045326 Validation acc: 0.695556\n",
      "Epoch: 22/1000 Iteration: 205 Train loss: 1.060812 Train acc: 0.621667\n",
      "Epoch: 23/1000 Iteration: 210 Train loss: 1.084982 Train acc: 0.591667\n",
      "Epoch: 23/1000 Iteration: 210 Validation loss: 1.011095 Validation acc: 0.718333\n",
      "Epoch: 23/1000 Iteration: 215 Train loss: 1.055872 Train acc: 0.641667\n",
      "Epoch: 24/1000 Iteration: 220 Train loss: 1.038498 Train acc: 0.641667\n",
      "Epoch: 24/1000 Iteration: 220 Validation loss: 0.972832 Validation acc: 0.745000\n",
      "Epoch: 24/1000 Iteration: 225 Train loss: 1.025283 Train acc: 0.646667\n",
      "Epoch: 25/1000 Iteration: 230 Train loss: 0.950369 Train acc: 0.670000\n",
      "Epoch: 25/1000 Iteration: 230 Validation loss: 0.930239 Validation acc: 0.766667\n",
      "Epoch: 26/1000 Iteration: 235 Train loss: 0.925225 Train acc: 0.713333\n",
      "Epoch: 26/1000 Iteration: 240 Train loss: 0.923589 Train acc: 0.670000\n",
      "Epoch: 26/1000 Iteration: 240 Validation loss: 0.883163 Validation acc: 0.773889\n",
      "Epoch: 27/1000 Iteration: 245 Train loss: 0.887181 Train acc: 0.691667\n",
      "Epoch: 27/1000 Iteration: 250 Train loss: 0.857177 Train acc: 0.698333\n",
      "Epoch: 27/1000 Iteration: 250 Validation loss: 0.832392 Validation acc: 0.795000\n",
      "Epoch: 28/1000 Iteration: 255 Train loss: 0.875697 Train acc: 0.676667\n",
      "Epoch: 28/1000 Iteration: 260 Train loss: 0.814531 Train acc: 0.718333\n",
      "Epoch: 28/1000 Iteration: 260 Validation loss: 0.779160 Validation acc: 0.808333\n",
      "Epoch: 29/1000 Iteration: 265 Train loss: 0.820755 Train acc: 0.708333\n",
      "Epoch: 29/1000 Iteration: 270 Train loss: 0.798339 Train acc: 0.758333\n",
      "Epoch: 29/1000 Iteration: 270 Validation loss: 0.726102 Validation acc: 0.827778\n",
      "Epoch: 30/1000 Iteration: 275 Train loss: 0.729569 Train acc: 0.743333\n",
      "Epoch: 31/1000 Iteration: 280 Train loss: 0.722833 Train acc: 0.736667\n",
      "Epoch: 31/1000 Iteration: 280 Validation loss: 0.674547 Validation acc: 0.831667\n",
      "Epoch: 31/1000 Iteration: 285 Train loss: 0.673115 Train acc: 0.783333\n",
      "Epoch: 32/1000 Iteration: 290 Train loss: 0.676867 Train acc: 0.770000\n",
      "Epoch: 32/1000 Iteration: 290 Validation loss: 0.624146 Validation acc: 0.847778\n",
      "Epoch: 32/1000 Iteration: 295 Train loss: 0.656898 Train acc: 0.746667\n",
      "Epoch: 33/1000 Iteration: 300 Train loss: 0.634756 Train acc: 0.760000\n",
      "Epoch: 33/1000 Iteration: 300 Validation loss: 0.576693 Validation acc: 0.852778\n",
      "Epoch: 33/1000 Iteration: 305 Train loss: 0.623274 Train acc: 0.768333\n",
      "Epoch: 34/1000 Iteration: 310 Train loss: 0.579742 Train acc: 0.791667\n",
      "Epoch: 34/1000 Iteration: 310 Validation loss: 0.532152 Validation acc: 0.859444\n",
      "Epoch: 34/1000 Iteration: 315 Train loss: 0.561034 Train acc: 0.813333\n",
      "Epoch: 35/1000 Iteration: 320 Train loss: 0.527807 Train acc: 0.808333\n",
      "Epoch: 35/1000 Iteration: 320 Validation loss: 0.490524 Validation acc: 0.865000\n",
      "Epoch: 36/1000 Iteration: 325 Train loss: 0.527679 Train acc: 0.821667\n",
      "Epoch: 36/1000 Iteration: 330 Train loss: 0.471822 Train acc: 0.848333\n",
      "Epoch: 36/1000 Iteration: 330 Validation loss: 0.452462 Validation acc: 0.870556\n",
      "Epoch: 37/1000 Iteration: 335 Train loss: 0.473013 Train acc: 0.828333\n",
      "Epoch: 37/1000 Iteration: 340 Train loss: 0.453588 Train acc: 0.838333\n",
      "Epoch: 37/1000 Iteration: 340 Validation loss: 0.417985 Validation acc: 0.877778\n",
      "Epoch: 38/1000 Iteration: 345 Train loss: 0.458486 Train acc: 0.835000\n",
      "Epoch: 38/1000 Iteration: 350 Train loss: 0.444841 Train acc: 0.836667\n",
      "Epoch: 38/1000 Iteration: 350 Validation loss: 0.387382 Validation acc: 0.883333\n",
      "Epoch: 39/1000 Iteration: 355 Train loss: 0.460267 Train acc: 0.840000\n",
      "Epoch: 39/1000 Iteration: 360 Train loss: 0.400515 Train acc: 0.886667\n",
      "Epoch: 39/1000 Iteration: 360 Validation loss: 0.360643 Validation acc: 0.885000\n",
      "Epoch: 40/1000 Iteration: 365 Train loss: 0.367223 Train acc: 0.858333\n",
      "Epoch: 41/1000 Iteration: 370 Train loss: 0.375984 Train acc: 0.863333\n",
      "Epoch: 41/1000 Iteration: 370 Validation loss: 0.336515 Validation acc: 0.891111\n",
      "Epoch: 41/1000 Iteration: 375 Train loss: 0.340604 Train acc: 0.876667\n",
      "Epoch: 42/1000 Iteration: 380 Train loss: 0.361643 Train acc: 0.871667\n",
      "Epoch: 42/1000 Iteration: 380 Validation loss: 0.315662 Validation acc: 0.891667\n",
      "Epoch: 42/1000 Iteration: 385 Train loss: 0.318835 Train acc: 0.891667\n",
      "Epoch: 43/1000 Iteration: 390 Train loss: 0.356998 Train acc: 0.861667\n",
      "Epoch: 43/1000 Iteration: 390 Validation loss: 0.297665 Validation acc: 0.897778\n",
      "Epoch: 43/1000 Iteration: 395 Train loss: 0.336684 Train acc: 0.873333\n",
      "Epoch: 44/1000 Iteration: 400 Train loss: 0.342601 Train acc: 0.878333\n",
      "Epoch: 44/1000 Iteration: 400 Validation loss: 0.281369 Validation acc: 0.900000\n",
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      "Epoch: 999/1000 Iteration: 8995 Train loss: 0.008298 Train acc: 1.000000\n",
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     ]
    }
   ],
   "source": [
    "validation_acc = []\n",
    "validation_loss = []\n",
    "\n",
    "train_acc = []\n",
    "train_loss = []\n",
    "\n",
    "with graph.as_default():\n",
    "    saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    iteration = 1\n",
    "   \n",
    "    # Loop over epochs\n",
    "    for e in range(epochs):\n",
    "        \n",
    "        # Loop over batches\n",
    "        for x,y in get_batches(X_tr, y_tr, batch_size):\n",
    "            \n",
    "            # Feed dictionary\n",
    "            feed = {inputs_ : x, labels_ : y, keep_prob_ : 0.5, learning_rate_ : learning_rate}\n",
    "            \n",
    "            # Loss\n",
    "            loss, _ , acc = sess.run([cost, optimizer, accuracy], feed_dict = feed)\n",
    "            train_acc.append(acc)\n",
    "            train_loss.append(loss)\n",
    "            \n",
    "            # Print at each 5 iters\n",
    "            if (iteration % 5 == 0):\n",
    "                print(\"Epoch: {}/{}\".format(e, epochs),\n",
    "                      \"Iteration: {:d}\".format(iteration),\n",
    "                      \"Train loss: {:6f}\".format(loss),\n",
    "                      \"Train acc: {:.6f}\".format(acc))\n",
    "            \n",
    "            # Compute validation loss at every 10 iterations\n",
    "            if (iteration%10 == 0):                \n",
    "                val_acc_ = []\n",
    "                val_loss_ = []\n",
    "                \n",
    "                for x_v, y_v in get_batches(X_vld, y_vld, batch_size):\n",
    "                    # Feed\n",
    "                    feed = {inputs_ : x_v, labels_ : y_v, keep_prob_ : 1.0}  \n",
    "                    \n",
    "                    # Loss\n",
    "                    loss_v, acc_v = sess.run([cost, accuracy], feed_dict = feed)                    \n",
    "                    val_acc_.append(acc_v)\n",
    "                    val_loss_.append(loss_v)\n",
    "                \n",
    "                # Print info\n",
    "                print(\"Epoch: {}/{}\".format(e, epochs),\n",
    "                      \"Iteration: {:d}\".format(iteration),\n",
    "                      \"Validation loss: {:6f}\".format(np.mean(val_loss_)),\n",
    "                      \"Validation acc: {:.6f}\".format(np.mean(val_acc_)))\n",
    "                \n",
    "                # Store\n",
    "                validation_acc.append(np.mean(val_acc_))\n",
    "                validation_loss.append(np.mean(val_loss_))\n",
    "            \n",
    "            # Iterate \n",
    "            iteration += 1\n",
    "    \n",
    "    saver.save(sess,\"checkpoints-cnn/har.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QwIgUF4fWqFlM50CUWmsWtouIiAJGSiq11sLAC01AKCJSnwJGJJVa64WagFBEJAMFjDTV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\nRQFDRESyooAhIiJZ+f+xsxtBHwg/xwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9492983c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and test loss\n",
    "t = np.arange(iteration-1)\n",
    "\n",
    "plt.figure(figsize = (6,6))\n",
    "plt.plot(t, np.array(train_loss), 'r-', t[t % 10 == 0], np.array(validation_loss), 'b*')\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rWdcyykt2Ut/cf4/2LSJSjLqtotnMSs3sXDN7GPg+8D1gAvDfwK/3ONIY5O24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\niEja/wOP9tWuLmG/EwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9492983908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Accuracies\n",
    "plt.figure(figsize = (6,6))\n",
    "\n",
    "plt.plot(t, np.array(train_acc), 'r-', t[t % 10 == 0], validation_acc, 'b*')\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"Accuray\")\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Evaluate on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.892500\n"
     ]
    }
   ],
   "source": [
    "test_acc = []\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    # Restore\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('checkpoints-cnn'))\n",
    "    \n",
    "    for x_t, y_t in get_batches(X_test, y_test, batch_size):\n",
    "        feed = {inputs_: x_t,\n",
    "                labels_: y_t,\n",
    "                keep_prob_: 1}\n",
    "        \n",
    "        batch_acc = sess.run(accuracy, feed_dict=feed)\n",
    "        test_acc.append(batch_acc)\n",
    "    print(\"Test accuracy: {:.6f}\".format(np.mean(test_acc)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
